The process of detection in image processing consists of the identification and location of areas within an image that bear further scrutiny because they many contain a target of interest. Classification is a more detailed and rigorous scrutiny of each detected area to decide if the detected area indeed contains a target with the proper characteristics.
The purpose of the detection and classification process for sonar images is to identify targets, such as sea mines, that pose a danger to navy assets so that the targets can be avoided or neutralized. Because of the mission, a primary goal for all detection and classification strategies is to maximize the joint probability of target detection and classification or P.sub.d P.sub.c as it is known. One inevitable result of this strategy is a high P.sub.d P.sub.c also produces a high number of false alarms or false target detections.
A false target is defined as an object that has been positively classified as a target, when in fact, no object is present in the groundtruth. Many of these false targets are actually part of image clutter formations. For sonar images, image clutter may represent actual physical bottom clutter or it may be created by the sonar data collection process. Examples of sonar image clutter include areas of high reverberation, high signal gain, sonar motion effects, and random speckle created by areas of constructive and destructive interference in the sonar signal return. To achieve a sufficiently low false target rate when analyzing sonar images, image clutter must be identified so that false targets resulting from image clutter do not hinder the visibility and correct classification of real targets. However, there are no sonar systems or automated detection schemes that use any kind of automated clutter recognition.
Currently, clutter recognition in sonar images is done by a human operator. The operator attempts to identify image clutter and the false targets resulting from same by visual examination of the image which is typically represented by an array of pixels on a display. The target-like objects are visually tracked through the image to see if they are part of a larger clutter structure. Another image clutter handling scheme involves counting the number of detections in an image. If the number of detections is high, the image is considered to be cluttered. The detection threshold is then continuously raised until the number of detections decreases to some manageable number. While the number of false targets is reduced, a number of detected real targets that were in the cluttered image can be eliminated from evaluation.